Similar to the above approach, Onoro and Sastre [1]} developed a scale aware counting model called Hydra cnn that can estimate object densities in a variety of crowded scenarios without any explicit geometric information of the scene. Hydra-CNN consists of 3 heads and a body, with each head learning features for a particular scale. Each head of the Hydra-CNN is constructed using a deep, fully convolutional neural network. The outputs of the heads are then concatenated and fed to the body, which consists of two fully connected layers followed by a rectified linear unit (ReLu), a dropout layer, and a final fully connected layer to estimate the object density map. While the different heads extract image descriptors at different scales, the body learns a high-dimensional representation that fuses the multi-scale information provided by the heads. This network design of Hydra CNN is inspired by the work of Li et al. [2]}. Finally, the network is trained with a pyramid of image patches extracted at multiple scales. The authors demonstrated through their experiments that the Hydra CNN performs successfully in scenarios and datasets with significant variations in the scene. The overview of the architecture is shown in fig:overviewhydra.
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